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New Product to Help Forecast Convective Initiation in the 1-6 Hour Time Frame Meeting September 12, 2007.

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Presentation on theme: "New Product to Help Forecast Convective Initiation in the 1-6 Hour Time Frame Meeting September 12, 2007."— Presentation transcript:

1 New Product to Help Forecast Convective Initiation in the 1-6 Hour Time Frame Meeting September 12, 2007

2 Introduction ● In recent years, strides have been made to “nowcast” Convective Initiation (CI) in the 0-1 hour time frame using radar and satellite trends. ● Beyond 2 hours, however, these methods perform very poorly at this task.

3 Introduction ● Proposed here is a new product that will help with the forecasting of air mass thunderstorms in the 1-6 hour time-frame by identifying potential sources of updrafts near differential heating boundaries.

4 Goal: ● Provide a tool (not a model) to help operational forecasters accurately identify and predict specific locations of CI, hours ahead of time, and on a daily basis. ● Requires a method that can be replicated: 1) Easily 2) Daily 3) In a timely manner

5 Hypothesis: ● Under conditions of… - Synoptically “calm” environment (surface winds < ~5 m/s) -Weak baroclinicity ● Thermal circulations will form along differential heating gradients, similar to “inland sea-breezes”. In the past, these thermals have been termed “Nonclassical Mesoscale Circulations”, or simply, NCMCs (Segal and Arritt, 1992). ● Given enough atmospheric instability, the location of these circulations will act as source regions for updrafts and, eventually, deep convection.

6 Factors to consider: 1) Vegetation 2) Surface Moisture (Recent Rainfall) 3) Insolation

7 Data 1) Vegetation: MODIS Level 3, Enhanced Vegetation Index (EVI) PROS 16-day composite, using the best quality data for each pixel out of 16 days Ensures virtually no cloud contamination of a scene Available at 1-km spatial resolutionCONS Oldest possible data for a geographic point is 16 days old* *Since Vegetation can be viewed as somewhat of a static field over short time-spans, this is acceptable

8 Data: MODIS Level 3 EVI

9 Data 2) Surface Moisture: Hydrologic Rainfall Analysis Project (HRAP) 24-hour Rainfall Estimates PROS Estimates derived from national array of Radars AND rain gauges Previous day’s data available the next morning Data that is older than 24 hours is thoroughly quality-checkedCONS Data averaged at 4-km spatial resolution No way of knowing from data exactly when in the 24-hour period the rain fell Does not account for crop Irrigation (hopefully this is would only be a small source of error)

10 Data: HRAP24-hour Rainfall Estimates

11 Data 3) Insolation: GOES-derived Insolation PROS Available at 2-km spatial resolution Operationally, could be derived within ~30 mins after most-recent GOES imageCONS Total insolation estimates depend upon interpolation of instantaneous values between images and their times. Missing or distorted images could contaminate or limit the accuracy of the final total insolation product.

12 Data: GOES-derived Insolation

13 Method Question: How do we combine variables of Vegetation, Surface Moisture, and Insolation to represent heating? –Studies by ‘Carlson et al. (1994)’, ‘Gillies et al. (1995)’, and ‘Carlson (2007)’ have shown the observed relationship between vegetation and surface moisture as it relates to heating. Figures from ‘Carlson (2007)’

14 Method -The purpose of these studies was to use NDVI and Radiometric Temperature, both derived from satellite imagery, to infer surface moisture. -But, what if we could use NDVI and surface moisture (inferred from location and amount of recent rainfall) to derive some sort of “heat index”. Figures from ‘Carlson (2007)’

15 Method

16 Method

17 Method ~ 0.33” Rain

18 Method 14%73%

19 Method 14%73% 0.60 For example, assume: EVI = %

20 Method Ok… 34% of what ??? This is where Insolation comes in 1)Scale ‘Total Insolation’ values between zero and the typical max value for a mid-summer’s day in this region (example: 0 – 17,000 kJ/m²) to values between 0 – )Multiply this scaled total insolation value by the derived ‘% of Sensible Heat’… in this case it is 34 %.

21 Method Example Given a site that: -Received 0.33” of rain in past 24-hours -Has an EVI value of Received 14,500 kJ/m² by 1:30pm 1) Scaling 0-17,000 kJ/m² to 0-255: 14,500 = 218 2) Percent of Sensible Heat from above = 34% Sensible Heat Index = (218 * 0.34) Sensible Heat Index = 74

22 Method -In this case, our Sensible Heat Index is 74 -Assuming that the rest of the insolation is partitioned as Latent Heat, we subtract the Sensible Heat Index value from the max index value of 255 to get a Latent Heat Index value: Latent Heat Index = 255 – 74 Latent Heat Index = 181 -We can repeat this procedure for every pixel within a scene.

23 Example: Sensible Heat Index

24 Example: Latent Heat Index

25 Example: Composite of Heat Indices

26 Future Plans - Look for combinations of Differential Heating gradients and atmospheric stability from a variety of case days to see in which instances this tool may prove useful. - Build an Antecedent Precipitation Index (API) to account for the contribution of rainfall from previous days (beyond 24-hours ago) - Perhaps, later include data sets of : 1) Vegetation Type 2) Elevation

27 Conclusion - Any Questions?


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